The Research of Aero-engine Digital Twin Model Based on Model-driven and Data-driven Fusion Method**National Science and Technology Major Project (2017-I-0003-0003)

2021 ◽  
Author(s):  
Kui Liu ◽  
Pan Wang ◽  
Qing Li
Keyword(s):  
Science And Technology ◽  
Data Driven ◽  
Fusion Method ◽  
Digital Twin ◽  
Model Driven ◽  
Model Based ◽  
Twin Model ◽  
National Science ◽  
Aero Engine ◽  
Major Project

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals On Digital Twin Condition Monitoring Approach for Drivetrains in Marine Applications

2019 ◽  
Author(s):  
Sigrid S. Johansen ◽  
Amir R. Nejad
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Test Rig ◽  
Digital Twin ◽  
Model Based ◽  
Twin Model ◽  
Digital Twins ◽  
Fidelity Model

Abstract A digital twin is a virtual representation of a system containing all information available on site. This paper presents condition monitoring of drivetrains in marine power transmission systems through digital twin approach. A literature review regarding current operations concerning maintenance approaches in todays practices are covered. State-of-the-art fault detection in drivetrains is discussed, founded in condition monitoring, data-based schemes and model-based approaches, and the digital twin approach is introduced. It is debated that a model-based approach utilizing a digital twin could be recommended for fault detection of drivetrains. By employing a digital twin, fault detection would be extended to relatively highly diagnostic and predictive maintenance programme, and operation and maintenance costs could be reduced. A holistic model system approach is considered, and methodologies of digital twin design are covered. A physical-based model rather than a data based model is considered, however there are no clear answer whereas which type is beneficial. That case is mostly answered by the amount of data available. Designing the model introduces several pitfalls depending on the relevant system, and the advantages, disadvantages and appropriate applications are discussed. For a drivetrain it is found that multi-body simulation is advised for the creation of a digital twin model. A digital twin of a simple drivetrain test rig is made, and different modelling approaches were implemented to investigate levels of accuracy. Reference values were derived empirically by attaching sensors to the drivetrain during operation in the test rig. Modelling with a low fidelity model showed high accuracy, however it would lack several modules required for it to be called a digital twin. The higher fidelity model showed that finding the stiffness parameter proves challenging, due to high stiffness sensitivity as the experimental modelling demonstrates. Two industries that could have significant benefits from implementing digital twins are discussed; the offshore wind industry and shipping. Both have valuable assets, with reliability sensitive systems and high costs of downtime and maintenance. Regarding the shipping industry an industrial case study is done. Area of extra focus is operations of Ro-Ro (roll on-roll off) vessels. The vessels in the case study are managed by Wilhelmsen Ship Management and a discussion of the implementation of digital twins in this sector is comprised in this article.

scholarly journals The Data Driven Surrogate Model Based Dynamic Design of Aero-Engine Fan Systems

2021 ◽  
Author(s):  
Yun-Peng Zhu ◽  
Jie Yuan ◽  
Z Q Lang ◽  
C W Schwingshackl ◽  
Loic Salles ◽  
...  
Keyword(s):  
Surrogate Model ◽  
Data Driven ◽  
Dynamic Design ◽  
Model Based ◽  
Aero Engine

Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts

2020 ◽  
Vol 14 (2) ◽  
pp. 1568-1579 ◽  
Author(s):  
Gray Bachelor ◽  
Eugenio Brusa ◽  
Davide Ferretto ◽  
Andreas Mitschke
Keyword(s):  
Digital Twin ◽  
Model Based
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